System and method for contextual drink detection

ABSTRACT

A system and method operable to monitor hydration and drink activity using one or more body-worn sensors and contextual information to more accurately detect drinking motions made by the user. The system and method can use an application encoded on a non-transitory computer-readable medium to receive disparate data from the one or more sensors to determine if the user has made a drinking motion. The analysis can be further refined using contextual information and a variable threshold to more accurately identify drinking motions.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/719,535, entitled “Drink Detection UsingRespiration Rate, Inter-Breath Intervals and Inter-Beat Intervals, andSolar Load Used To Improve Fluid Loss Estimation,” that was filed in theU.S. Patent and Trademark Office on Aug. 17, 2018, all of which isincorporated herein by reference in its entirety for all purposes.

FIELD

The present disclosure relates to systems and methods related tonon-invasive drink detection.

BACKGROUND

Wearable devices have been used by performance athletes and amateurs tomonitor physical activities. The devices can be configured to determinea hydration level for the wearer and communicate with a mobile device orexternal computer to analyze data captured at the devices. Existingmethods of hydration and drink detection cannot accurately identifydrinking motions or can misidentify other motions as a drinking motionthus underestimating the user's hydration level.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description will be better understood when readin conjunction with the appended drawings. For the purpose ofillustration, there is shown in the drawings certain examples of thepresent disclosure. It should be understood, however, that the presentinventive concept is not limited to the precise examples and featuresshown. The accompanying drawings, which are incorporated in andconstitute a part of this specification, illustrate an implementation ofapparatuses consistent with the present inventive concept and, togetherwith the description, serve to explain advantages and principlesconsistent with the present inventive concept.

FIG. 1A illustrates an example of a wearable device according to thepresent disclosure.

FIG. 1B illustrates an example of a mobile device according to thepresent disclosure.

FIG. 1C illustrates an example of a remote computer according to thepresent disclosure.

FIG. 1D is a schematic diagram of an example wearable device systemaccording to the present disclosure.

FIG. 2 is a schematic diagram of an example mobile device systemaccording to the present disclosure.

FIG. 3A illustrates exemplary accelerometer data used to detect matterinput from the x-axis of the accelerometer.

FIG. 3B illustrates exemplary accelerometer data used to detect matterinput from the y-axis of the accelerometer.

FIG. 3C illustrates exemplary accelerometer data used to detect matterinput from the z-axis of the accelerometer.

FIG. 4 shows an exemplary diagram of accelerometer data which has beenprocessed by dynamic time warping.

FIG. 5 shows an example using heart rate to detect a drink event.

FIG. 6 shows a diagram of drink events corresponding to hydrationstatus.

FIG. 7 is a diagram of a drink detection algorithm application,according to the present disclosure.

FIG. 8 is one example of a displayed drink monitoring graphic accordingto the present disclosure.

FIG. 9 is one example of a displayed drink monitoring graphic accordingto the present disclosure.

FIG. 10 is flowchart illustrating a method or process for contextualdrink detection according to the present disclosure.

FIG. 11 is flowchart illustrating a sub-process of in-context drinkdetection according to the present disclosure.

DETAILED DESCRIPTION

Several definitions that apply throughout this disclosure will now bepresented. The term “comprising” means “including, but not necessarilylimited to”; it specifically indicates open-ended inclusion ormembership in a so-described combination, group, series and the like.“About” refers to almost, nearly, on the verge of, or withoutsignificant deviation from the numeric representation. For example,about 20 can be 20, or a small deviation from 20. “Coupled” refers tothe linking or connection of two objects. The coupling can be direct orindirect. An indirect coupling includes connecting two objects throughone or more intermediary objects. Coupling can also refer to electricalor mechanical connections. Coupling can also include linking withoutphysical contact. While “skin” is used throughout the disclosure, anysuitable “tissue” of the user can be interchangeably used with “skin.”

The present disclosure relates to systems and methods, using datagathered from body-worn sensors, environmental data, and contextual datato detect when a user takes a drink, that and provide information and/orprompt to the user.

In various aspects, the systems and methods use wearable devicesconfigured to communicate with external computing devices, including butnot limited to mobile devices, computers, and data servers. In anotheraspect, the wearable device can include a sensor that is configured tomeasure motion of the user to detect when a user is drinking asubstance. In yet, another aspect, the systems and methods are able todetermine if the user should take additional drinks to meet a desiredthreshold or hydration level.

The present disclosure endeavors to solve a variety of problems in theindustry. The present disclosure includes the ability to detect drinkevents. The present disclosure additionally includes the ability toestimate the volume of liquid ingested by a user during a drink event.The present disclosure also allows the monitoring of the hydration of auser.

The present disclosure includes a system and device for determiningdrink events using non-invasive techniques. Drink events includedrinking fluids, such as water, soda, or any other fluid that the bodyintakes for hydration.

The present disclosure can be implemented in one or more of the devicesand/or systems described herein. In one example, the present disclosureincludes a wearable device. As used herein, a wearable device is anydevice that is in contact or close proximity to a user of the device.Examples of wearable devices include a wrist worn device, arm, handand/or finger worn device, clothing, an athletic aid, a monitor, abracelet, a band, a ring, and/or compression sleeves. The wearabledevices can be configured to have a wireless communication or wiredcommunication interface to allow for exchange of data. In at least oneexample, the wearable device is operable to be electronically coupled toa mobile device. In at least one example, the wearable device can beconfigured to include a user notification component that providesinstructions to the user. The user notification component can be adisplay, an audio device, a vibration device, and/or a visual indicator.In other examples, the user notification component can be omitted andthe wearable device can communicate instructions to the mobile devicefor communication of the instructions to the user.

The term mobile device can include a device that has a processor and amemory. The mobile device in at least some examples includes a display.Additionally, the mobile device can include a communication componentthat is operable to allow for communication with the mobile device to anexternal device. The wearable device can also be configured tocommunicate with one or more external sensor components. The wirelesscommunication can be performed using short range wireless communicationprotocols such as BLUETOOTH, ZIGBEE, Advanced and Adaptive NetworkTechnology (ANT+), WI-FI, Radio Frequency Identification (RFID), or thelike.

Maintaining proper hydration is important not only to assure thatathletes can maintain peak performance but also to maintain one's goodhealth, cosmetic appearance, and/or wellness. Proper hydration isimportant to keep cognitive function and/or to help manage one's weight.It is also critical in maintaining one's good health, includingpreventing headaches, coronary heart disease, kidney stones, and/orcancer.

Hydration maintenance is best achieved under long-term and/or continuousmonitoring of vital signs, rendering it a task that is best performed bya device that is in prolonged contact with the user, such as a wearabledevice. Moreover, wearable devices are capable of integrating a largerange of sensors and/or a processor, storing the signals generated bythese sensors in its internal memory for later processing and/orcommunicating these signals, and/or the results of its internalprocessing. The signals can also be communicated to the user and/or tothe world at large via wireless (or wired) communications, includingstoring data in the cloud for visualization, for further processing by aserver, and/or for storage in a larger database, making aggregate dataavailable to additional processing and/or to the development of newalgorithms.

Maintaining proper hydration requires estimating the balance betweenfluid gains and/or fluid losses. That is to say, by monitoring drinksone can estimate the input side of hydration monitoring, resulting inthe health and/or wellness benefits commonly associated with themaintenance of proper hydration.

In an example, a mobile device system includes a mobile device and/or awearable device operable to detect drink events and/or monitor hydrationfor a user. The mobile device has at least one sensor which can detectmotion of the mobile device. The wearable device can detect a biologicalindicator of the user and can transmit the data to the mobile device.The mobile device and/or another component in the system correlates thebiological indicator of the user with the detected motion signal(s) intime to determine if one or more drink events has occurred and createsan input log for each drink event. In at least one example, the mobiledevice also determines a net balance of the user based on the input logsand/or output logs for expelled fluids by, for example, vomiting,urination, defecation, and/or perspiration. The net balance can providethe benefit of helping to improve the health and/or well-being of a userby being within a predetermined range, below, or above a predeterminedthreshold. For example, the net balance can be used to help a user toreach health-related goals such as, for example, staying well hydrated.To be well hydrated, a user should be above a hydration threshold.Although the system and/or device are described with respect to a mobiledevice, the system and/or device can be entirely operable on a wearabledevice.

In another example, a wearable device operable to detect drink events ofa user includes at least one motion sensor operable to detect motionand/or record motion signals of the wearable device. The wearable devicecan further include a processor coupled to the at least one motionsensor and/or at least one biological sensor coupled to the processorand is operable to detect one or more biological indicators of the user.The wearable device can also include a memory that is operable to storeinstructions to cause the wearable device to do one or more of thefollowing: obtain at least one biological indicator of the user,correlate the biological indicator of the user with the detected one ormore motion signals, and determine that a drink event is detected basedon the correlation between the detected motion signals and/or the atleast one biological indicators.

In another example, a mobile device can be coupled with the wearabledevice and can include a processor. The mobile device can also include adisplay coupled to the processor and operable to display data receivedfrom the processor. The mobile device can also include a memory coupledto the processor and operable to store instructions to cause theprocessor to do one or more of the following: obtain, from the wearabledevice, at least one of the one or more biological indicators of theuser, correlate the at least one biological indicators of the user withthe detected one or more motion signals, and determine that a drinkevent is detected based on the correlation between the detected one ormore motion signals and/or the at least one biological indicators.

FIG. 1A illustrates an example of a wearable device 122 according to thepresent disclosure. The wearable device 122 can include a transmitter126, a component processor 128, one or more biological sensors 124, adisplay 177, and input device 179, a memory 186, and/or one or moreadditional sensors 132. The wearable device 122 can include and/or becoupled with at least one external sensor component which can be one ormore of: scales, water bottles, glucose measurement systems, bloodpressure monitors, pulse oximeters, respiration rate monitors, tissueoximeters, respirators, electrocardiogram monitors, and/or the like. Thewearable device 122 can also be enabled to wirelessly communicate withother devices.

The one or more biological sensors 124 can be coupled to the componentprocessor 128 and is operable to detect a biological indicator 206 of auser 208. The transmitter 126 is operable to transmit a detectedbiological indicator 206 to the at least one communication component 118of the mobile device 100, a remote computer 168, and/or another externaldevice. The biological sensors 124 can include one or more of athermometer component 144 operable to measure a temperature of skin ofthe user 208 and/or surrounding ambient temperature, a near-infraredspectrometer (NIRS) 146 operable to monitor chromophores that constitutea tissue of the user 208, a bioimpedance monitor 148, aphotoplethysmography (PPG) monitor 150, a heart rate monitor 152, anambient light sensor 154, an atmospheric pressure sensor 156, analtitude sensor 158, a relative humidity sensor 160, a scale 162, amicrophone 164, a localization sensor 166, a clock 178, an event marker180, a ultra violet (UV) sensor 182, and/or a camera 184. Furthermore,the one or more biological sensors can be operable to detect one or morebiological indicators, which can include a heart rate, a heart ratevariation, a blood pressure, a respiration rate, a blood oxygensaturation level, muscle oxygenation level, skin temperature, skinperfusion, skin impedance, galvanic skin response, blood pressure,tissue perfusion, blood flow, blood volume, extracellular fluid, tissuehydration, tissue hydration variation, intracellular fluid,photoplethysmograph, images, videos and/or sounds associated with adrink event. For example, the signals of a PPG monitor can be processedto measure blood oxygen saturation, heart rate, heart rate variation,blood pressure and/or respiration rate. As such, a PPG monitor can havethe function of multiple individual sensors, and the device 122 can bemore compact.

The additional sensors 132 include one or more motion sensors 133. Themotion sensors 133 can include an inertial motion unit (IMU) 134, anaccelerometer 136, gyroscope 138, and/or magnetometer 140. Theadditional sensors 132 can also include a global position systemcomponent 142 to assist in determining a physical location of the user.

FIG. 1B illustrates an example of a mobile device 100 according to thepresent disclosure. The mobile device 100 includes a display 102, aprocessor 104, an input unit 106, at least one sensor 108, at least onecommunication component 118, and/or a memory 120. The at least onesensor 108 is operable to detect motion of the mobile device 100. The atleast one sensor 108 can be a gyroscope 110, an accelerometer 112, amagnetometer 114, and/or a global positioning system component 116. Theat least one communication component 118 is operable to receive and/ortransmit data from a wearable device 122 and/or a remote computer 168.The processor 104 is coupled to the at least one sensor 108 and/or theat least one communication component 118.

FIG. 1C illustrates an example of a remote computer 168. The remotecomputer 168 can include one or more of: one or more processors 170, oneor more storage devices 172, one or more memories 174, or one or moreexternal Input/Output (IO) interfaces 176. The remote computer 168 canbe a cloud based computer system 212, shown in FIG. 2 or a cloud storageand data processing system 105, shown in FIG. 1D.

FIG. 1D is a schematic diagram of an example wearable device system 101according to the present disclosure. The wearable device system 101 caninclude the mobile device 100, the wearable device 122, and/or a cloudstorage and data processing system 105. In at least one example, thecloud storage and data processing system 105 can include one or more ofthe components described in relation to the remote computer 168 of FIG.1C. Further, an internet 143 is operable to allow communication betweenthe mobile device 100, the wearable device 122, and/or the cloud storageand data processing system 105. The wearable device 122 can include oneor more of: a processor 107 operable to communicate with a memory 109,one or more sensors 111, one or more algorithms 113, internetcommunication 117, and/or a wireless transmitter and receiver 119.

The internet 143 can refer to the Internet, an intranet, and/or anotherwired or wireless communication network. For example, the internet 143can include a Mobile Communications (GSM) network, a code divisionmultiple access (CDMA) network, 3rd Generation Partnership Project (GPP)network, an Internet Protocol (IP) network, a wireless applicationprotocol (WAP) network, a WiFi network, a satellite communicationsnetwork, and/or an IEEE 802.11 standards network, as well as variouscommunications thereof. Other conventional and/or later developed wiredand/or wireless networks can also be used.

In one example, the one or more sensors 111 collects data from a user208 and the processor 107 processes the data and sends at least onenotification 115 to the user 208. The at least one notification 115 canbe provided to the user 208 via one or more of a display, lights, sound,vibrations, and/or buzzers. The at least one notifications 115 canfurther be associated with achieving one or more predefined goals,wherein the one or more predefined goals are health or well-being. Inone example, the predefined goal can be to improve well-being bymaintaining a level of hydration in order to increase a user's overallhealth. In another example, the predefined goal can be to stay hydratedwithin an allowable range of net hydration balance, thus preventingdisease states related to dehydration. In other examples, the predefinedgoal can include one or more goals, which can be both diet and/orexercise related. In other examples, the predefined goals can includeskin beauty and/or mental alertness goals. In other examples, thepredefined goals can include athletic performance goals, such aspre-hydrating in preparation for a given athletic event.

The mobile device 100 includes a mobile application 127 operable tocommunicate with one or more of a memory 125, a wireless transmitter andreceiver 121, a metadata 129, a one or more sensors 131, and an internetcommunication 123. In an example, the mobile device 100 is controlled bythe mobile application 127 that collects additional data from the one ormore sensors 131 and also collects the metadata 129. The metadata 129can be, for example, from one or more of a user's calendar, contacts,and/or geographic location.

The cloud storage and data processing system 105 can include one or morebackend algorithms 141 operable to communicate with a long-term userdatabase 135, one or more outside databases 139, and/or an internetcommunication 137. The cloud storage and data processing system 105enables the storage of long-term user data into the long-term userdatabase 135 and/or the execution of more complex backend algorithms141. These backend algorithms 141 also benefit from the long-term dataderived from other users that are similar to a specific user. Theinformation derived from the backend algorithms 141 are provided to theuser 208 via the mobile application 127 and/or directly to the wearabledevice 122.

The memory of the mobile device 100, the one or more wearable devices122, the remote computer 168, the cloud-based computer system 212,and/or the storage device 214, can include volatile and/or non-volatilememory, e.g., a computer-readable storage medium such as a cache, randomaccess memory (RAM), read only memory (ROM), flash memory, and/or othermemory to store data and/or computer-readable executable instructionssuch as a portion and/or component of the drink detection application702.

Furthermore, The memory of the mobile device 100, the one or morewearable devices 122, the remote computer 168, the cloud-based computersystem 212, and/or the storage device 214 can be volatile media,nonvolatile media, removable media, non-removable media, and/or othermedia or mediums that can be accessed by a general purpose and/orspecial purpose computing device. For example, the memory section 508can include non-transitory computer storage media and/or communicationmedia. Non-transitory computer storage media further can includevolatile, nonvolatile, removable, and/or non-removable media implementedin a method and/or technology for the storage (and retrieval) ofinformation, such as computer/machine-readable/executable instructions,data and/or data structures, engines, program modules, and/or otherdata. Communication media can, for example, embodycomputer/machine-readable/executable, data structures, program modules,algorithms, and/or other data. The communication media can also includean information delivery technology. The communication media can includewired and/or wireless connections and/or technologies and can be used totransmit and/or receive wired and/or wireless communications.

FIG. 2 illustrates an example mobile device system 200. The mobiledevice system 200 can include a mobile device 100, one or more wearabledevices 122, a remote computer 168, a cloud-based computer system 212,and/or a storage device 214. The components can communicate with eachother as indicated by the arrows shown. For example, the mobile device100 can communicate with one or more of the cloud based computer system212, the remote computer 168, and/or the one or more wearable devices122.

In one example according to the present disclosure, the one or morewearable devices 122 can be in the form of a wrist device 210 operableto be worn on a wrist of a user 208. The wrist device 210 can alsoinclude additional sensors 132 (shown in FIG. 1A) to measure motion of awrist and/or record motion signals corresponding to the measured motion.

The motion sensors 133 provide the wearable device 122 with a set ofmotion signals indicative of the position and/or motion of the limb inwhich the wearable device 122 is worn—typically the wrist. The motionsignals are then processed, for example, either by the IMU and/or by theprocessor, to generate a new signal indicative of significant motion.For example, the motion signal can include the sum of the squares of theaccelerations measured in the x, y and/or z axes of the accelerometerpresent in the IMU. Once the motion signal is detected above a certainthreshold level (for example, at least 1.5 times higher than the levelof motion detected when the user is at rest) the wearable device 122processes the other signals, for example additional x, y, and/or zaccelerations, provided by the motion sensors 133 in order to identifythe motion being undertaken by the user.

Drinking motions often consists of the user moving his/her dominant armtoward their mouths. To prevent confusing drinking motions with othersimilar motions a library of motions can be created by having a numberof users perform similar motions in a large number of trials. Themotions are labeled and identified as to what motions they represent ata given point in time and with a given duration. As such, machinelearning classification algorithms, such as k-nearest neighbors, supportvector machines, decision tress, time-delay neural networks, linearand/or quadratic discriminant analysis, can be used to distinguishbetween true drinking motions from other (false-positive) motions. Theresulting classifier algorithms and their pertinent parameters can beloaded onto the wearable device memory 186 and the algorithm is executedby the processor 128 whenever a significant motion is detected,resulting in a new signal indicative of a drinking motion.

In various embodiments, the method for the detection for drinking usesdata acquired from both motion sensors and/or optical sensors, such asbut not limited to the motion sensors 133 and the PPG sensor 150,respectively.

In at least one example, the detected motion signals then can betransmitted to the mobile device 100 and/or remote computer 168. Thewrist device 210 can also be operable to communicate with the mobiledevice 100 and/or other connected device via a wired and/or wirelesscommunication connection. For example, the wrist device 210 canwirelessly communicate with the mobile device 100, the remote computer168, and/or a cloud based computer system 212 indicated by the arrowsshown in FIG. 2. In another example, the wrist device 210 cancommunicate with the mobile device 100, the remote computer 168, and/orthe cloud based computer system 212 via a wired connection. The wristdevice 210 can be entirely self-sufficient. In other examples, the wristdevice 210 can be without a connection to the internet and/or mobiledevice 100. The data transmitted to the cloud based computer system 212and/or other long-term memory storage device can be stored for futureuse and/or processed to provide information useful to a user 208.

The memory 120 of the mobile device 100 can be operable to store furtherinstructions to cause the mobile device 100 to display a recommendationto the user 208 for a next drink event that includes an input activity,an input timing, and/or an input duration. In at least one example, thewearable device 122 can display the information without the presence ofthe mobile device 100. For example, the mobile device 100 and/orwearable device 122 can display instructions to drink two ounces ofwater in about five minutes while a user 208 is running. Furthermore,the memory 120 of the mobile device 100 can cause the mobile device 100to display a determined drink event on a display of the mobile deviceand receive confirmation and/or modification of the displayed drinkevent. Also, the display 102 can display data received from the remotecomputer 168, the cloud based computer system 212, and/or the one ormore wearable devices 122.

The display 102 can also display an output event that includes one ormore of: output activity, output timing, and/or output duration. Theoutput events can be perspiration, urination, defecation, excretion,coughing, sneezing, vomiting, blood loss, plasma loss, ascetic fluidloss, fluid redistribution, diarrhea, temperature loss, temperaturechange, insensible fluid loss, fat loss, muscle loss, bone loss,calories burnt, sleep loss, attention loss, alertness loss, yellingand/or crying (indicators of mood loss or the like).

Furthermore, the net balance of input and/or output can be displayed.The long-term monitoring of the net balance of input events and/oroutput events can be used by the mobile device 100 and/or wearabledevice 122 to provide the user 208 with relevant information regardingtheir health, wellness, and/or cosmetic appearance. The beneficialinformation includes hydration balance. For example, the mobile device100 and/or the wearable device 122 can use sensed estimates of drinkevents and/or fluid intake to notify the user to continue drinkingfluid.

For example, as shown in FIGS. 3A-3C, the three plots 300, 302, 303depict the x, y, and z axis of accelerometers, demonstrating thataccelerometer data can be used to detect drink events. In the exampleshown in FIGS. 3A-3C, the acceleration of the wrist of three differentsubjects was monitored while the subjects ingested twenty differentboluses of an electrolyte solution with volumes varying from 0.5 to 4oz., each, showing a high degree of repeatability from drink motion todrink motion, thus demonstrating that drink motions can be effectivelyidentified using accelerations measured by a wrist-worn motion sensors133.

In at least one example, the velocity with which the user's hands moveduring drinking events can vary considerably from person-to-person andeven within the same person. To ensure that drinking motions arerepresented in a similar way under most conditions, the accelerationdata can be segmented and/or pre-processed using techniques such asdynamic time warping, dynamic tiling, and/or fast Fourier transformsafter zero padding. FIG. 4 shows an example of dynamic time warping formapping an input signal 402 into another temporal signal 404 by matchingfeatures that are common in both versions of the signal as depicted bydashed lines 406. This common mapping allows the direct comparison ofthe transformed signal with other signals available in a signal libraryrepresentative of drinking motions.

Moreover, the motion signals can be processed using adaptive algorithmsthat are sensitive to temporal variations, such as time-delay and/orfinite impulse response (FIR) neural networks, and/or long short termmemory networks (LSTMN). Every time a user newly dons a wearable device,the wearable device 122 can be located in a slightly different location.Similarly, different users can drink in slightly different ways. Thus,adaptive signal processing methods can be employed to adjust foruser-to-user and/or wear-to-wear variations. For example, a rotationmatrix can be used to re-orient the wearable device 122 regardless ofthe orientation in which the wearable device 122 is worn.

The drink and/or non-drink class of activities can also be distinguishedusing algorithms such as Normal Activity Recognition, ActivityThresholds, and/or k-nearest neighbors. In Normal Activity Recognition,for example, specific activities can be recognized by first computingthe surface normal and then comparing the temporal variation of thesurface normal vector against those of pre-trained activities stored ina local library. In the k-nearest neighbors classification method, aninput sample point can be assigned the category label of the k nearestset of previously classified points. For example, using anearest-neighbor algorithm, drinking events can be distinguished fromnon-drinking events with an accuracy better than 92%, a sensitivitybetter than 89% and a specificity better than 87%.

Referring now to FIG. 5 shows an example of a biological signal used toindicate the time and/or volume of a drink event. In the example shownin FIG. 5, the heart rate of a user was monitored using a heart ratesensor 152 while the user ingested six boluses of an electrolytesolution: 250 ml in the first bolus 502 and 153 ml in each of thesubsequent five boluses 504, 506, 508, 510, 512, corresponding to atotal of 14 ml per kg of total body mass. The dashed lines 514 mark thebeginning of each one of the drink events. The user's heart rate surgesshortly after each drink and the surge in heart rate lasts approximatelythe duration of the drinking event. Therefore, during a drink event boththe amplitude and/or duration of the heart rate surge (above baseline)increase as a function of the volume of fluid ingested. In at least oneexample, the volume of ingested fluid can be estimated by thearea-under-the-curve between the surge and/or baseline signals.

As shown in FIG. 6, an example of how drink detection events can be usedto help determine the hydration status of a user. The diagram 600 shownin FIG. 6 depicts a Markov decision process, where the present state isindependent of past states. Methods for solving Markov decisionprocesses include dynamic Bayesian networks and maximum likelihoodestimators. Expectation-maximization algorithms, such as theBaldi-Chauvin algorithm and/or the Markov-chain Monte Carlo algorithm,can also be included, as well as on-line learning algorithms such as theBaum-Welch algorithm, the Bayesian online algorithm and/or the MeanPosterior approximation algorithm. Also, the current state of the usercan not be known a priori and only inferred from observations of theiractions, which is called a “hidden Markov model” and can also be solvedby the algorithms listed above, with or without modifications.

In the example diagram 600 shown in FIG. 6, a set of possible userstates include saturated 620, hydrated 614, dehydrated 608, and verydehydrated 602 which are represented by circles. Each user state cancorrelate to a range of hydration levels. For each user state, there aresets of two possible user actions such as drink 624, 618, 612, 606 andno drink 622, 616, 610, 604 which are represented by rectangles. Arrowsshow possible future states once a user has undertaken a given action,with solid arrows representing transition probabilities that are higherthan those of dashed arrows, which are higher than those represented bydotted arrows. For example, when the user state is very dehydrated 602,the user can not drink 604, and the probability of the user being verydehydrated 602 is high. The user can also drink 606, and the probabilityof the user transitioning to dehydrated 608 or remaining very dehydrated602 are high. The probability of the user transitioning to hydrated 614is medium, and the probability of the user transitioning to saturated islow. Note that for exemplary purposes, only the transition probabilityarrows associated with the Drink action 606 and No Drink action 604associated with the Very Dehydrated state 602, and the transitionprobability arrows associated with the Drink action 624 and No Drinkaction 622 associated with the Saturated state 620 are shown. All othertransition probability arrows associated with all actions, all states,do exist but are omitted from diagram 600 for clarity.

The use of at least one sensor 108 and/or biological sensors 124 can beused in isolation or in combination. For example, the mobile device 100can obtain data from the IMU 134, wherein the determination of one ormore drink events is based on the obtained data from the IMU 134 withrespect to time and/or heart rate. In at least one example, the time ofa heart rate spike and/or motion detected from the IMU 134 can indicatethe start of a drink event. Furthermore, predetermined motions, such asa return to a position prior to initiation of the input event, forexample, can indicate an end of a drink event.

In at least one example according to the present disclosure, a mobiledevice 100 is operable to determine habits of a user 208 and to makerecommendations regarding changes in habits. The mobile device 100includes one or more internal sensors 108 operable to detect at leastone of a motion of the mobile device 100 and/or location of the device.The mobile device 100 also includes a processor 104 coupled to the oneor more internal sensors 108 and/or a display 102 coupled to theprocessor 104 and operable to display data 314 received from theprocessor 104. The communication component 118 is coupled to theprocessor 104 and is operable to receive data 314 from at least one of:a remote computer 168 or one or more external sensor components 122operable to detect a biological indicator 206. The mobile device 100further includes a memory 120 coupled to the processor 104 and isoperable to store instructions to cause the processor to perform theprocess of logging input events. In at least one example, the wearabledevice 122 is operable to determine habits of a user 208 and makerecommendations 320 regarding changes in habits without the use of amobile device 100 and/or a remote computer 168.

In at least one example according to the present disclosure, a mobiledevice system 200 is operable to provide recommendations on input for auser 208 including one or more of: a mobile device 100 and/or anexternal sensor 122. The mobile device 100 includes at least one sensor108 operable to detect motion of the mobile device 100 and/or at leastone communication component 118 operable to receive data 314 from one ormore external sensor components 122 or remote computer 168. The mobiledevice 100 also includes a processor 104 coupled to the at least onesensor 108 and/or the at least one communication component 118. Themobile device system 200 also can include one or more of: an externalsensor component 122 having a component processor 128; a biologicalsensor 124 coupled to the component processor 128 and operable to detecta biological indicator 206 of the user 208; and/or a transmitter 126operable to transmit the detected biological indicator 206 to the atleast one communication component 118 of the mobile device 100. Theremote computer 168 includes a processor 170 and/or a memory 174 that isoperable to store instructions to perform the process of logging inputevents.

FIG. 7 illustrates a block diagram of a computer readable media (CRM)700 that can be read and/or executed by the processors of a computingdevice, such as the wearable device 122, the mobile deice 100, theremote computer 168, and/or the cloud storage and data processing system105, according to various embodiments. The CRM 700 can also be stored inmemory of the computing devices and can contain the drink detectionapplication 702, other user interface and/or application. The computerreadable media can include volatile media, nonvolatile media, removablemedia, non-removable media, and/or another available medium that can beaccessed by the respective processors of the computing devices. By wayof example and not limitation, the computer readable media comprisescomputer storage media and/or communication media. Computer storagemedia includes non-transitory storage memory, volatile media,nonvolatile media, removable media, and/or non-removable mediaimplemented in a method and/or technology for storage of information,such as computer/machine-readable/executable instructions, datastructures, program modules, or other data. Communication media canembody computer/machine-readable/executable instructions, datastructures, program modules, or other data and include an informationdelivery media and/or system, both of which are hardware.

The remote computer 168 can be a laptop computer, a smartphone, apersonal digital assistant, a tablet computer, a standard personalcomputer, and/or another processing device. The remote computer 168 caninclude a display 177, such as a computer monitor, for displaying dataand/or graphical user interfaces. Each computing devices 122, 100, 168,and/or 105, can also include an input device 179, such as a camera, akeyboard and/or a pointing device (e.g., a mouse, trackball, pen, and/ortouch screen) to enter data into and/or interact with graphical and/orother types of user interfaces. In an exemplary embodiment, the display177 and/or the input device 179 can be incorporated together as a touchscreen of the smartphone and/or tablet computer.

Furthermore, the at least wearable device 122 can display on the display177 a graphical user interface (or GUI) application to generate agraphical user interface on the display. The graphical user interfacecan be generated by one or more modules of the drink detectionapplication 702. The graphical user interface enables a user of the atleast one mobile device 100 and/or user-worn device 122 to interact withthe drink detection application 702.

The drink detection application 702 can be a component of an applicationand/or service executable by the at least one client computing device104 and/or the motor carrier computing device 105 and/or the at leastone server computing device 102. For example, the drink detectionapplication 702 can be a single unit of deployable executable codeand/or a plurality of units of deployable executable code. According toone aspect, the drink detection application 702 can include onecomponent that can be a web application, a native application, and/or amobile application (e.g., an app) downloaded from a digital distributionapplication platform that allows users to browse and/or downloadapplications developed with mobile software development kits (SDKs)including the App Store and/or GOOGLE PLAY®, among others.

As shown in FIG. 7, the drink detection application 702 can include anumber of modules executable by at least one of the processors 104, 107,128, and/or 170 of at least one of the mobile device 104, wearabledevice 107, and/or the remote computing device 168. The modules includebut are not limited to a drink motion extraction module 704, acardiopulmonary module 706, an activity context module 708, a contextualthreshold module 710, and/or a user output module 712. Althoughidentified as individual modules for the purposes of illustration, thefunctionality of the modules 704-712 can be combined and/or overlap withthe functionality of other modules. In addition, the modules 704-712 canbe located on a single CRM 214 and/or distributed across multiplecomputer readable media on multiple computing devices.

In various aspects, the modules 704-712 can reside and/or be executed atone or more computing device of the system 200. For example, in oneembodiment, all data processing and/or analysis takes place on thewearable device 122.

In another example, the majority but not all data processing and/oranalysis takes place on the wearable 122 device. In this example,processing and/or analysis for a small sample set (e.g. only the mostrecent data measures ˜1000 data measurements or less) is performed onthe wearable device. Analysis of larger data sets can be performed atmore robust computing devices. As such, the wearable device 122 requireslimited memory and/or processing capabilities.

In yet another example, little to no processing takes place on thewearable device 122. The data captured by the sensors 132 and/or 134 onthe wearable device 122 is streamed to the mobile device 100. In variousaspects, the data processing and/or analysis occurs on the mobile device100, the remote computer 168, and/or at the cloud processing system 105.

The drink motion extraction module 704 receives and/or processes motiondata captured by one or more accelerometers of the sensors 132.Suspected drink motions are identified from the drink motion data bydynamic time warping. In particular, the drink motion extraction module704 uses machine learning techniques. In one aspect, the module 704 istrained using data including known drinking and/or sucking motions,known motions often confused with drinking motions, (e.g. combing hair,opening doors with vertical handles), and/or known non-drinking motions.

The motion detection algorithms are combinations of dynamic time warping(DTW) algorithms and/or time-series versions of classificationalgorithms. These can include but are not limited to linear discriminantanalysis (LDA), quadratic discriminant analysis (QDA), and/or a supportvector machine (SVM). The drink motion extraction module 704 generatesspecialized signals for subsequent analysis at the contextual thresholdmodule 710.

To determine that a drink event occurred with greater accuracy, thewearable device 122 can correlate biological indicators from biologicalsensors 124 along with the motion signals from the motion sensors 133.As such, the drink detection application 702 also includescardiopulmonary module 706 that receives cardiopulmonary data such as aheart rate (HR), cardio-inter-beat intervals (IBI), breathing rate (BR)and pulmonary-inter-breath interval (ibi), and/or other dynamicpulmonary movements, such as a cough and/or sneeze. In various aspects,the data signals are derived from time-frequency analysis of PPG dataand IMU data.

The cardiopulmonary module 706 generates signals to highlight knownswallowing and sucking signals that can be derived from the heart rateand breathing. The events identified also include swallowing and/or sipmotions, straw sucking, and chugging motions that change the respiratorymotions of the system user.

As readily understood, the respiration rate of the user undergoesperiodic inhale/exhale cycles. Thus, drinking events that interruptthese cycles can be identified by the cardiopulmonary module 706. In oneaspect, the cardiopulmonary module 706 generates Poincare plotsillustrating heart rate variability that can detect abnormalities in thecardiac cycle and/or the breathing cycle. In particular, while cardiac(HRV) Poincare’ plots only take into account RR intervals, breathingcycles also include exhales, requiring three dimensions to represent acomplete cycle in a Poincare’ plot, along the dimensions of ibi^(t−1)_(insp), ibi^(t) _(exp), and ibi^(t) _(insp).

For example, a user stops breathing in order to take a drink. As such,the biological indicator can include a breathing rate, and/or pauses inbreathing. Additionally, to compensate for the momentary loss inarterial blood oxygenation due to the user stopping breathing, theuser's circulatory system increases oxygen delivery by increasing theblood flow and/or by increasing the heart rate. As such, the heart ratevariability (HRV) also increases while the user respiration rate suffersa decrease. Therefore, the biological indicator can include a surge inheart rate and/or changes in HRV.

Drinking may not be the only action that causes a change in heart rateover a period of time. The period of time can be, for example, 3seconds, 10 seconds, or 30 seconds. Changes in heart rate can also becaused by other actions that require compensation through a change inblood volume. For example, when a person stands up suddenly their heartrate usually increases to assure proper oxygenation to their head at ahigher elevation. As such, the wearable device 122 can utilizeadditional biological indicators indicative of a drink event. Forexample, the thermometer component 144 can provide biological indicatorsthat can include changes in skin and/or core temperatures, changes inskin and/or core temperatures with respect to ambient temperature asdrinks tend to be at a different temperature compared to ambienttemperature. For example, drinks tend to be cold when the ambienttemperature is hot and vice-versa. Another biological indicator can bean increase in skin perfusion. The skin typically works as a waterreservoir. As such, when a user dehydrates, their peripheral vasculaturecan constrict, leading to reduced perfusion in the skin and/orextremities. Conversely, when a user drinks and/or rehydrates, perfusioncan increase. As such, a near-infrared spectrometer 146 can measuretissue hydration and/or tissue perfusion, and the wearable device 122can determine that a drink event is detected when an increase in tissueperfusion is measured. Other examples of biological indicators that canbe used to detect drink events include the sounds and/or imagesassociated with drinking, which can be captured by microphones, stilland/or video cameras embedded on the wearable device 122 and/or themobile device 100.

Drinking also can increase the amount of blood plasma and the balancebetween intracellular and extracellular fluids, and that change inratio, such as tissue hydration variation, can be measured usingbioimpedance monitors 148. During water absorption events, water istypically first ingested orally before being absorbed by the digestivetract, at which point the water is transferred into the blood plasma.From the blood plasma, water is distributed throughout the body toarterioles and/or capillaries, where water becomes extracellular fluidbefore being osmotically absorbed by the cells in the body, thusbecoming intracellular fluid. Cell membranes contain fatty tissue andare thus highly resistant to electric current while fluid is highlyconductive. Thus, measuring the bioimpedance of the body provides onewith an estimate of the ratio of intracellular over extracellular fluidcontent, thereby providing us with an estimate of fluid flow within theuser body. The bioimpedance monitors 148 can measure biologicalindicators including galvanic skin response, skin resistance, skinconductance, electrodermal response, psychogalvanic reflex, skinconductance response, sympathetic skin response, skin conductance level,and/or electrodermal activity.

The drink detection application 702 also includes an activity contextmodule 708 that receives data/signals from the both the motion sensors133 and/or physiological signals from the biological sensors 124. In oneaspect, the activity context module 708 processes the received data tofurther determine the conditions under which the measurements were made.For example, when the user is sleeping, it is less likely that a drinkwill be had. Conversely, during a period of exercise, there is anincreased likelihood of detecting a drinking motion. In other aspects,the activity context module 708 uses the received data measurements todetermine when the user is likely sitting, walking, or exercising,riding in a car among others. In another aspect, the activity contextmodule can access a consensus set of patterns built from a large numberof subjects in relevant demographic populations and/or geographiclocations. Similarly, the contextual data can be adaptive and/orpersonalized for the user. For example, the types and/or context ofdrinks could be personalized, such as indicating when a user alwaysdrinks from a straw or only drinks while seated around mealtimes.

The drink detection application 702 also includes a contextual thresholdmodule 710 varies a threshold for confirming or rejecting a suspectingdrinking motion based on the outputs of the drink motion extractionmodule 704, the cardiopulmonary module 706, and/or the activity contextmodule 708.

In one aspect, motion, heart rate, and/or breathing rate measurementsand/or changes in the measurements can independently signal the presenceof a drink. For example, if the device is not worn on the hand that isdrinking we expect to see HR/BR modulations without motion. Similarly,if other physiological events are taking place during a drink we can seemotion without a strong HR/BR modulation. So each of these methodologieshas a threshold associated with it (i.e. motion threshold (T_(m)) andHR/BR threshold (T_(o))).

In various aspects, the thresholds can be contextually adapted and/orvaried to better account for the likelihood that a drink motion is tooccur. For example, if drinks have recently been detected, then thedrink detection threshold can be lowered, thus increasing theprobability that a temporally proximate drink motion is classified as anactual drink motion. Conversely, the thresholds during periods of sleepare made very high, thus decreasing the likelihood that a potentialdrink motion is classified as an actual drink motion.

As described, contextual clues and/or user activity can modulate thethreshold level based on the quality of the gathered measurements and/orthe probability of drinking during each activity. For example, themeasurements can be more precise and/or of higher quality when the useris sitting. Alternatively, the measurements can be less accurate and/orlower quality when the user is exercising. The thresholds can be furthermodulated or set based upon cross modality confirmation. For example,cross modality confirmation refers to the combined use of thresholds forboth motion-based detection and cardiopulmonary based detection toaccept or tally drinks that can otherwise be rejected when compared witha motion threshold or cardiopulmonary threshold, individually. In oneaspect, cross modality confirmation can lead to the acceptance ortallying of drinks that do not meet the threshold criterion formotion-based detection and do not meet the threshold for cardiopulmonarybased detection. In this aspect, the motion-based detection thresholdcan be reduced when the cardiopulmonary based detection threshold is ina region around, but still lower than the motion-based threshold.Similarly the cardiopulmonary threshold may be reduced based on itsproximity to the motion-based threshold.

In addition to detecting drinking events, the drink detectionapplication 702 can be further configured to estimate a volume of liquidingested with each drink. According to one aspect the volume of eachsip, chug, and/or drink can be estimated using the motion dynamics ofthe drink as well as the cardiopulmonary characteristics. The number ofdrinks and/or the total estimated volume can both be stored and/orpresented to users.

Additionally, to improve the detection of drink events, the wearabledevice 122 and/or the mobile device 100 can access specific events onthe user's calendar and/or social media accounts to aid in thedetermination of whether or not the user is undergoing a drink event ata given time. For example, references to words such as “Lunch,”“Dinner,” and “Breakfast” are associated with a higher probability ofdrinking while words such as “Run,” “Workout,” and “Spin class” are moreclosely associated with output events that lead to a loss of hydrationvolume due to increased physical activity, resulting in a higher loss ofliquids due to increased perspiration and/or respiration rate. Thewearable device 122 and/or the mobile device 100 can also use one ormore of the user's contacts and/or calendar events to determine whetherthe user is in a location and/or the presence of one or more people withwhom the user experiences drink events.

Additionally, the wearable device 122 and/or the mobile device 100 canuse the user's physical location to assist in the estimate of whetherthe user is likely to undergo an input or output event. For example, theprobability of the user drinking is higher when the user is in arestaurant, bar, cafe, and/or cafeteria. Indicators of physical locationcan also include ambient light and/or UV exposure detectors measured,for example, by ambient light sensors 154, indicating whether the useris indoors or outdoors. Additional examples of physical locationindicators can include knowledge of previous locations regularly visitedby the user and stored in memory 186, the use of altitude sensors 158,atmospheric pressure sensors 156, and/or relative humidity sensors 160detectors to determine whether the user is indoors or outdoors and/or atwhat floor of a given building, and captured images and/or videos bycamera 184. The wearable device 122 and/or the mobile device 100 canalso send one or more notifications 133 to the user 208. Thenotifications 133 can also be provided to the user 208 via one or moreof a display, lights, sound, vibrations, and/or buzzers.

The drink detection application 702 also includes a user output module712. The user output module 712 can provide an indication to the userthat a drink should be taken. The output module 712 can also generate adisplay, such as one embodiment of a display 800 as shown in FIG. 8. Asshown, the display 800 includes a graph and/or chart to shown the numberof drinks taken relative to an optimal number of drinks. In otheraspects, the graph could be linear, curved (like a ring), and/or someother representation. By way of example, the graph can include display aplurality of drinking glasses, a large vessel of water that is filled inrelation to drinks taken, and/or a plant that grows and/or straightensup as the user drinks. In one aspect, the total scale could berepresentative of the total number of drinks and/or a desired totalvolume of liquid and each drink could fill in a portion of the graph.

Another embodiment of a display 900 that can be generated by the useroutput module 712 is shown in FIG. 9. In this display, the historicalhydration and/or thirst levels of the user as a function of time can beshown. As indicated, the hydration and/or thirst level of the uservaries over time. The hydration and/or thirst level can decrease basedupon fluid lost during sweat and alternatively can increase in responseto the drinks taken by the user.

In other aspects, user output module 712 can meter the displayed outputsuch that allow one drink and/or some predetermined volume of liquiddrank per unit of time is displayed in order to encourage the user ofthe system to take time between drinks. Similarly, the user outputmodule 712 can provide direct feedback of drinking events and/orhydration levels. For example, the user output module 712 can initiate avibration notification, audible tone, and/or an illuminated signal onthe wearable device 122 and/or the mobile device 100 when a goal volumefor the hour, day, week, or other period is met.

In one aspect, the user output module 712 is adapted to log or recordand/or indicate the time and/or volume of each drink. The output module712 can further receive input from the user to edit the drink log byadding or rejecting tallied drinks and/or to adjust the calculatedvolume of liquid drank by the user.

FIG. 10 illustrates a block diagram detailing a method of drinkdetection using from motion and/or optical sensors according to at leastone instance of the present inventive concept. Method 1000 can usevarious machine-learning based pattern classification processes todistinguish between physical drinking motions and a drink respirationpattern, both of which may be identified as drinks. As shown, the method1000 is described and implemented with respect to the systems, devices,and applications as described with respect to FIGS. 1-9. While themethod 1000 is shown and described with respect to blocks 1002-1016, itis within the scope of this disclosure to implement any number ofblocks, including omission of one or more blocks of method 1000 orinclusion of additional blocks not specifically described with respectto method 1000. Further, while blocks are described sequentially, nospecific order is implied nor required. Method 1000 can begin at block1002.

According to one aspect, at block 1002, motion data and/orcardiopulmonary data are captured by sensors, such as 133 and/or 124. Atblock 1004, a determination is made at the drink motion extractionmodule 704, to determine if an actual user motion is detected based onthe captured motion data. If the determination is affirmative, thenfurther analysis is performed by the drink motion extraction module atblock 1006 to determine if the identified motion is a drink motion atblock 1008. If the motion is identified as a drink motion at block 1008,then a drink is detected and reported to the user at 1010. As indicatedby 1016, whether or not a motion is detected, the method returns toblock 1002 to continuously monitor the sensors 133 and/or 124.

Referring now back to block 1004, if the no clear motion is detected atthe drink motion extraction module 704, the data is further processed atthe cardiopulmonary module 706 at block 1012. At the cardiopulmonarymodule, a respiration inter-breath-interval (IBI) is calculated and usedin a machine-learning based pattern classification analysis to determineif the pattern is a drink as block 1014. If the pattern is notidentified as a drink, the method returns, as indicated by 1016, toblock 1002 where the motion and/or optical sensors a continuouslycollecting motion and/or cardiopulmonary data. Conversely, if the motionis identified as a drink, then a drink is tallied and reported to theuser at block 1010.

In various embodiments, the drink detection determinations performed atblock 1008 and/or block 1014 further includes contextual data alsogathered by the sensors 133 and/or 124. In particular, the contextualdata is processed at the activity context module 708 and/or thecontextual threshold module 710 to generate or modify a contextualthreshold that can be used to aid in the detection of an actual drinkingmotion.

FIG. 11 illustrates a block diagram detailing a method for in contextdrink detection according to at least one instance of the presentinventive concept. Method 1100 is described and implemented with respectto the systems, devices, and applications as descried with respect toFIGS. 1-10. In one aspect, the method 1100 is performed by the executionof the contextual threshold module 710 of FIG. 7, at a processor. Whilethe method 1100 is shown and described with respect to blocks 1102-1118,it is within the scope of this disclosure to implement any number ofblocks, including omission of one or more blocks of method 1100 orinclusion of additional blocks not specifically described with respectto method 1100. Further, while blocks are described sequentially, nospecific order is implied nor required. Method 1100 can begin at block1102.

At block 1102, a first determination is made whether the system user isasleep. If the output of the activity context module 708 indicates thatthe user is sleeping, the contextual threshold module 710 increases thethreshold such that a majority of motions are classified as non-drinkmotions at block 1104. Conversely, if the activity context module 708indicates that the user is not sleeping, at block 1106 the contextualthreshold module 710 processes the input data against a motion threshold(T_(m)) by the function: P(Drink|IMU)>T_(m). As previously noted, thethreshold T_(m) varies and can be raised or lowered depending on theoutput of the activity context module 708. In various aspects, baselinethresholds are derived from population and/or demographic based modelscompiled from previously collected data. In various aspects, the datacan also be gathered from previous user data and/or data from otherusers most like the current user.

If the determined motion is greater than T_(m) then a drink is talliedat block 1108. Conversely, if the motion is less than T_(m), then themethod proceeds to block 1110, where the motion is further analyzed todetermine if the received data correlates to a known non-drink motion.If the motion correlates to a non-drink motion, then the received datais classified as a non-drink at block 1112. However, if the receiveddata does not correlate to a known non-drink motion, then the processproceeds to block 1114, where the contextual threshold module 710 alsoprocesses data related to cardiopulmonary function. In particular, thecontextual threshold module 710 further processes the cardiopulmonarydata against a HR/BR threshold (T_(o)) by the function: P(Drink|HR/BR)>T_(o). As previously noted, the threshold T_(o) varies and can beraised or lowered depending on the output of the activity context module708. Using the received motion data, cardiopulmonary data, and/oractivity context data from the activity context module 708, thecontextual threshold module 710 makes a final drink or non-drinkdetermination as indicated by block 1116 and block 1118, respectively,which are stored in a database and/or cloud storage 214 and can beprovided to the user.

Numerous examples are provided herein to enhance understanding of thepresent disclosure. A specific set of statements are provided asfollows.

Statement 1: A system for contextual drink detection for a user wearingone or more body-worn sensors, the system comprising: a memory; and atleast one processor to: receive data from the one or more body-wornsensors; determine if the received data includes motion data orbiological data; wherein when the received data includes motion data,the processor to: classify the motion data as a pre-determined motionpattern; determine if the classified pre-determined motion patterncorresponds to a drink; wherein when the classified pre-determinedmotion pattern is identified as the drink, the processor to tally thedrink, transmit a notification to a computing device of a user wearingthe body-worn sensors, and poll the body-worn sensors for additionaldata; and wherein when the classified pre-determined motion pattern isnot identified as the drink, the processor to poll the body-worn sensorsfor the additional data; and wherein when the received data includesbiological data, the processor to: determine at least one of arespiration rate, heart rate, or heart rate variability of the user;determine if the at least one respiration rate, heart rate, or heartrate variability of the user corresponds to the drink; wherein when theat least one respiration rate, heart rate, or heart rate variability ofthe user corresponds to the drink, the processor to tally the drink,transmit a notification to a computing device of a user wearing thebody-worn sensors, and poll the body-worn sensors for the additionaldata; and wherein when the at least one respiration rate, heart rate, orheart rate variability of the user does not correspond to the drink, theprocessor to poll the body-worn sensors for the additional data.

Statement 2: The system according to Statement 1, wherein thedetermination of the drink motion is informed by contextual data andsystem further comprising: the processor to: receive contextual data;generate a variable contextual threshold based on the contextual data;wherein the received data is more likely to be identified as the drinkmotion when the contextual threshold is lower than a baseline value; andwherein the received data is less likely to be identified as the drinkmotion when the contextual threshold is greater than a baseline value.

Statement 3: The system according to Statement 2, further comprising:the processor to: determine if the user of the body-worn sensors issleeping; and determine if the motion data corresponds to a knownnon-drink motion.

Statement 4: The system of any one according to Statements 1-3, whereinthe motion data is pre-processed using dynamic time warping.

Statement 5: The system according to Statement 1, wherein the one ormore body-worn sensors comprises at least one motion sensor and one ormore biological sensors.

Statement 6: The system according to Statement 5, wherein the at leastone motion sensor includes at least one of an inertial motion unit, anaccelerometer, a magnetometer, and a gyroscope.

Statement 7: The system according to Statement 5, wherein the one ormore biological sensors includes a photoplethysmography monitor, and thebiological data comprises at least one of blood oxygen saturation data,heart rate data, heart rate variation data, blood pressure data, andrespiration rate data.

Statement 8: The system of any one according to Statements 1-7, whereinat least one of the motion data and the biological data is processed bya machine learning classification technique.

Statement 9: The system of any one according to Statements 1-7, whereinthe transmitted notification to the user comprises a graphic display.

Statement 10: The system according to Statement 9, wherein the graphicdisplay indicates a number of drinks taken by the user.

Statement 11: The system according to Statement 9, wherein the graphicdisplay indicates the user hydration level over time.

Statement 12: The system according to Statement 1, wherein motion data,biological data, or both motion data and biological data are used toidentify the drink.

Statement 13: A method for contextual drink detection of a user wearingone or more body-worn sensors; the method comprising: receiving, by atleast one processor, data from the one or more body-worn sensors;determining, by the at least one processor, if the received dataincludes motion data or biological data; wherein when the received dataincludes motion data: classifying, by at least one processor, the motiondata as a pre-determined motion pattern; determining, by the at leastone processor, if the classified pre-determined motion patterncorresponds to a drink; wherein when the classified pre-determinedmotion pattern is identified as the drink by the at least one processor,tallying the drink, transmitting a notification to a computing device ofa user wearing the body-worn sensors, and polling the body-worn sensorsfor additional data; and wherein the classified pre-determined motionpattern is not identified as the drink by the at least one processor,polling the body-worn sensors for the additional data; and wherein whenthe received data includes biological data: determining, by the at leastone processor, at least one of a respiration rate, heart rate, or heartrate variability of the user; determining, by the at least oneprocessor, if the at least one of a respiration rate, heart rate, orheart rate variability corresponds to the drink motion; wherein when theat least one respiration rate, heart rate, or heart rate variability ofthe user is determined to correspond to the drink by the at least oneprocessor, tallying the drink, transmitting a notification to acomputing device of a user wearing the body-worn sensors, and pollingthe body-worn sensors for the additional data; and wherein when the atleast one respiration rate, heart rate, or heart rate variability of theuser is determined not to correspond to the drink by the at least oneprocessor, polling the body-worn sensors for the additional data.

Statement 14: The method according to Statement 13, wherein thedetermination of the drink motion is informed by contextual data; methodfurther comprising: receiving, by the at least one processor, contextualdata; generating, by the at least one processor, a variable contextualthreshold based on the contextual data; identifying, by the at least oneprocessor, the received data as the drink motion when the contextualthreshold is lower than a baseline value; and identifying, by the atleast one processor, the received data as a non- drink motion when thecontextual threshold is greater than a baseline value.

Statement 15: The method according to Statement 14, further comprising:determining, by the at least one processor, if the user of the body-wornsensors is sleeping; and determining, by the at least one processor, ifthe motion data corresponds to a known non-drink motion.

Statement 16: The method of any one according to Statements 13-15,further comprising pre-processing, by the at least one processor, themotion data using dynamic time warping.

Statement 17: The method according to Statement 13, wherein the one ormore body-worn sensors comprises at least one motion sensor and one ormore biological sensors.

Statement 18: The method according to Statement 17, wherein the at leastone motion sensor includes at least one of an inertial motion unit, anaccelerometer, a magnetometer, and a gyroscope.

Statement 19: The method according to Statement 17, wherein the one ormore biological sensors includes a photoplethysmography monitor, and thebiological data comprises at least one of blood oxygen saturation data,heart rate data, heart rate variation data, blood pressure data, andrespiration rate data.

Statement 20: The method of any one according to Statements 13-19,further comprising processing, by the at least one processor, at leastone of the motion data and the biological data using a machine learningclassification technique.

Statement 21: The method of any one according to Statements 13-19,wherein the transmitted notification to the user comprises generating,by the at least one processor, a graphic display.

Statement 22: The method according to Statement 21, wherein the graphicdisplay indicates a number of drinks taken by the user.

Statement 23: The method according to Statement 21, wherein the graphicdisplay indicates the user hydration level over time.

Statement 24: The method according to Statement 13, wherein motion data,biological data, or both motion data and biological data are used toidentify the drink.

Statement 25: A non-transitory computer-readable storage medium, havinginstructions for contextual drink detection stored thereon that, whenexecuted by a computing device cause the computing device to performoperations, the operations comprising: receiving data from the one ormore body-worn sensors; determining if the received data includes motiondata or biological data; wherein when the received data includes motiondata: classifying the motion data as a pre-determined motion pattern;determining if the classified pre-determined motion pattern correspondsto a drink; wherein the classified pre-determined motion pattern isidentified as the drink by the at least one processor, tallying thedrink, transmitting a notification to a computing device of a userwearing the body-worn sensors, and polling the body-worn sensors foradditional data; and wherein when the classified pre-determined motionpattern is not identified as the drink by the at least one processor,polling the body-worn sensors for the additional data; and wherein whenthe received data includes biological data: determining at least one ofa respiration rate, heart rate, or heart rate variability of the user;determining if the at least one of a respiration rate, heart rate, orheart rate variability corresponds to the drink motion; wherein when theat least one respiration rate, heart rate, or heart rate variability ofthe user is determined to correspond to the drink , transmitting anotification to a computing device of a user wearing the body-wornsensors, and polling the body-worn sensors for the additional data; andwherein when the at least one respiration rate, heart rate, or heartrate variability of the user does not correspond to the drink, pollingthe body-worn sensors for the additional data.

Statement 26: The non-transitory computer-readable storage mediumaccording to Statement 25, wherein the determination of the drink motionis informed by contextual data; method further comprising: receivingcontextual data; generating a variable contextual threshold based on thecontextual data; identifying the received data as the drink motion whenthe contextual threshold is lower than a baseline value; and identifyingthe received data as a non-drink motion when the contextual threshold isgreater than a baseline value.

Statement 27: The non-transitory computer-readable storage mediumaccording to Statement 26, further comprising: determining if the userof the body-worn sensors is sleeping; and determining if the motion datacorresponds to a known non-drink motion.

Statement 28: The non-transitory computer-readable storage medium of anyone according to Statements 25-27, further comprising pre-processing themotion data using dynamic time warping.

Statement 29: The non-transitory computer-readable storage mediumaccording to Statement 25, wherein the one or more body-worn sensorscomprises at least one motion sensor and one or more biological sensors.

Statement 30: The non-transitory computer-readable storage mediumaccording to Statement 29, wherein the at least one motion sensorincludes at least one of an inertial motion unit, an accelerometer, amagnetometer, and a gyroscope.

Statement 31: The non-transitory computer-readable storage mediumaccording to Statement 29, wherein the one or more biological sensorsincludes a photoplethysmography monitor, and the biological datacomprises at least one of blood oxygen saturation data, heart rate data,heart rate variation data, blood pressure data, and respiration ratedata.

Statement 32: The non-transitory computer-readable storage medium of anyone according to Statements 25-31, further comprising processing atleast one of the motion data and the biological data using a machinelearning classification technique.

Statement 33: The non-transitory computer-readable storage medium of anyone according to Statements 25-31, wherein the transmitted notificationto the user comprises generating a graphic display.

Statement 34: The non-transitory computer-readable storage mediumaccording to Statement 33, wherein the graphic display indicates anumber of drinks taken by the user.

Statement 35: The non-transitory computer-readable storage mediumaccording to Statement 33, wherein the graphic display indicates theuser hydration level over time.

Statement 36: The non-transitory computer-readable storage mediumaccording to Statement 25, wherein motion data, biological data, or bothmotion data and biological data are used to identify the drink.

Methods according to the above-described examples and statements can beimplemented using computer-executable instructions that are stored orotherwise available from computer readable media. Such instructions cancomprise, for example, instructions and/or data which cause and/orotherwise configure a general purpose computer, special purposecomputer, or special purpose processing device to perform a certainfunction or group of functions. Portions of computer resources used canbe accessible over a network. The computer executable instructions canbe, for example, binaries, intermediate format instructions such asassembly language, firmware, and/or source code. Examples ofcomputer-readable media that can be used to store instructions,information used, and/or information created during methods according todescribed examples include magnetic or optical disks, flash memory, USBdevices provided with non-volatile memory, networked storage devices,and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, and so on. Functionality described herein also can beembodied in peripherals and/or add-in cards. Such functionality can alsobe implemented on a circuit board among different chips and/or differentprocesses executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and/or other structures for supportingsuch computing resources are means for providing the functions describedin these disclosures.

Although a variety of examples and/or other information was used toexplain aspects within the scope of the appended claims, no limitationof the claims should be implied based on particular features and/orarrangements in such examples, as one of ordinary skill would be able touse these examples to derive a wide variety of implementations. Furtherand although some subject matter can have been described in languagespecific to examples of structural features and/or method blocks, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features and/or acts. Forexample, such functionality can be distributed differently and/orperformed in components other than those identified herein. Rather, thedescribed features and/or blocks are disclosed as examples of componentsof systems and/or methods within the scope of the appended claims.

It will be appreciated that variations of the above-disclosed and/orother features and/or functions, or alternatives thereof, can bedesirably combined into many other different systems and/orapplications. Also that various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein can besubsequently made by those skilled in the art which are also intended tobe encompassed by the following claims.

1. A system for contextual drink detection for a user wearing one ormore body-worn sensors, the system comprising: a memory; and at leastone processor to: receive data from the one or more body-worn sensors;determine if the received data includes motion data or biological data;wherein when the received data includes motion data, the processor to:classify the motion data as a pre-determined motion pattern; determineif the classified pre-determined motion pattern corresponds to a drink;wherein when the classified pre-determined motion pattern is identifiedas the drink, the processor to tally the drink, transmit a notificationto a computing device of a user wearing the body-worn sensors, and pollthe body-worn sensors for additional data; and wherein when theclassified pre-determined motion pattern is not identified as the drink,the processor to poll the body-worn sensors for the additional data; andwherein when the received data includes biological data, the processorto: determine at least one of a respiration rate, heart rate, or heartrate variability of the user; determine if the at least one respirationrate, heart rate, or heart rate variability of the user corresponds tothe drink; wherein when the at least one respiration rate, heart rate,or heart rate variability of the user corresponds to the drink, theprocessor to tally the drink, transmit a notification to a computingdevice of a user wearing the body-worn sensors, and poll the body-wornsensors for the additional data; and wherein when the at least onerespiration rate, heart rate, or heart rate variability of the user doesnot correspond to the drink, the processor to poll the body-worn sensorsfor the additional data.
 2. The system of claim 1, wherein thedetermination of the drink motion is informed by contextual data andsystem further comprising: the processor to: receive contextual data;generate a variable contextual threshold based on the contextual data;wherein the received data is more likely to be identified as the drinkmotion when the contextual threshold is lower than a baseline value; andwherein the received data is less likely to be identified as the drinkmotion when the contextual threshold is greater than a baseline value.3. The system of claim 2, further comprising: the processor to:determine if the user of the body-worn sensors is sleeping; anddetermine if the motion data corresponds to a known non-drink motion. 4.The system of any one of claim 1, wherein the motion data ispre-processed using dynamic time warping.
 5. The system of claim 1,wherein the one or more body-worn sensors comprises at least one motionsensor and one or more biological sensors.
 6. The system of claim 5,wherein the at least one motion sensor includes at least one of aninertial motion unit, an accelerometer, a magnetometer, and a gyroscope.7. The system of claim 5, wherein the one or more biological sensorsincludes a photoplethysmography monitor, and the biological datacomprises at least one of blood oxygen saturation data, heart rate data,heart rate variation data, blood pressure data, and respiration ratedata.
 8. The system of any one of claim 1, wherein at least one of themotion data and the biological data is processed by a machine learningclassification technique.
 9. The system of any one of claim 1, whereinthe transmitted notification to the user comprises a graphic display.10. The system of claim 9, wherein the graphic display indicates anumber of drinks taken by the user.
 11. The system of claim 9, whereinthe graphic display indicates the user hydration level over time. 12.The system of claim 1, wherein motion data, biological data, or bothmotion data and biological data are used to identify the drink.
 13. Amethod for contextual drink detection of a user wearing one or morebody-worn sensors; the method comprising: receiving, by at least oneprocessor, data from the one or more body-worn sensors; determining, bythe at least one processor, if the received data includes motion data orbiological data; wherein when the received data includes motion data:classifying, by at least one processor, the motion data as apre-determined motion pattern; determining, by the at least oneprocessor, if the classified pre-determined motion pattern correspondsto a drink; wherein when the classified pre-determined motion pattern isidentified as the drink by the at least one processor, tallying thedrink, transmitting a notification to a computing device of a userwearing the body-worn sensors, and polling the body-worn sensors foradditional data; and wherein the classified pre-determined motionpattern is not identified as the drink by the at least one processor,polling the body-worn sensors for the additional data; and wherein whenthe received data includes biological data: determining, by the at leastone processor, at least one of a respiration rate, heart rate, or heartrate variability of the user; determining, by the at least oneprocessor, if the at least one of a respiration rate, heart rate, orheart rate variability corresponds to the drink motion; wherein when theat least one respiration rate, heart rate, or heart rate variability ofthe user is determined to correspond to the drink by the at least oneprocessor, tallying the drink, transmitting a notification to acomputing device of a user wearing the body-worn sensors, and pollingthe body-worn sensors for the additional data; and wherein when the atleast one respiration rate, heart rate, or heart rate variability of theuser is determined not to correspond to the drink by the at least oneprocessor, polling the body-worn sensors for the additional data. 14.The method of claim 13, wherein the determination of the drink motion isinformed by contextual data; method further comprising: receiving, bythe at least one processor, contextual data; generating, by the at leastone processor, a variable contextual threshold based on the contextualdata; identifying, by the at least one processor, the received data asthe drink motion when the contextual threshold is lower than a baselinevalue; and identifying, by the at least one processor, the received dataas a non-drink motion when the contextual threshold is greater than abaseline value.
 15. The method of claim 14, further comprising:determining, by the at least one processor, if the user of the body-wornsensors is sleeping; and determining, by the at least one processor, ifthe motion data corresponds to a known non-drink motion.
 16. The methodof any one of claim 13, further comprising pre-processing, by the atleast one processor, the motion data using dynamic time warping.
 17. Themethod of claim 13, wherein the one or more body-worn sensors comprisesat least one motion sensor and one or more biological sensors.
 18. Themethod of claim 17, wherein the at least one motion sensor includes atleast one of an inertial motion unit, an accelerometer, a magnetometer,and a gyroscope.
 19. The method of claim 17, wherein the one or morebiological sensors includes a photoplethysmography monitor, and thebiological data comprises at least one of blood oxygen saturation data,heart rate data, heart rate variation data, blood pressure data, andrespiration rate data.
 20. The method of any one of claim 13, furthercomprising processing, by the at least one processor, at least one ofthe motion data and the biological data using a machine learningclassification technique.
 21. The method of any one of claim 13, whereinthe transmitted notification to the user comprises generating, by the atleast one processor, a graphic display.
 22. The method of claim 21,wherein the graphic display indicates a number of drinks taken by theuser.
 23. The method of claim 21, wherein the graphic display indicatesthe user hydration level over time.
 24. The method of claim 13, whereinmotion data, biological data, or both motion data and biological dataare used to identify the drink.
 25. A non-transitory computer-readablestorage medium, having instructions for contextual drink detectionstored thereon that, when executed by a computing device cause thecomputing device to perform operations, the operations comprising:receiving data from the one or more body-worn sensors; determining ifthe received data includes motion data or biological data; wherein whenthe received data includes motion data: classifying the motion data as apre-determined motion pattern; determining if the classifiedpre-determined motion pattern corresponds to a drink; wherein theclassified pre-determined motion pattern is identified as the drink bythe at least one processor, tallying the drink, transmitting anotification to a computing device of a user wearing the body-wornsensors, and polling the body-worn sensors for additional data; andwherein when the classified pre-determined motion pattern is notidentified as the drink by the at least one processor, polling thebody-worn sensors for the additional data; and wherein when the receiveddata includes biological data: determining at least one of a respirationrate, heart rate, or heart rate variability of the user; determining ifthe at least one of a respiration rate, heart rate, or heart ratevariability corresponds to the drink motion; wherein when the at leastone respiration rate, heart rate, or heart rate variability of the useris determined to correspond to the drink, transmitting a notification toa computing device of a user wearing the body-worn sensors, and pollingthe body-worn sensors for the additional data; and wherein when the atleast one respiration rate, heart rate, or heart rate variability of theuser does not correspond to the drink, polling the body-worn sensors forthe additional data.
 26. The non-transitory computer-readable storagemedium of claim 25, wherein the determination of the drink motion isinformed by contextual data; method further comprising: receivingcontextual data; generating a variable contextual threshold based on thecontextual data; identifying the received data as the drink motion whenthe contextual threshold is lower than a baseline value; and identifyingthe received data as a non-drink motion when the contextual threshold isgreater than a baseline value.
 27. The non-transitory computer-readablestorage medium of claim 26, further comprising: determining if the userof the body-worn sensors is sleeping; and determining if the motion datacorresponds to a known non-drink motion.
 28. The non-transitorycomputer-readable storage medium of any one of claim 25, furthercomprising pre-processing the motion data using dynamic time warping.29. The non-transitory computer-readable storage medium of claim 25,wherein the one or more body-worn sensors comprises at least one motionsensor and one or more biological sensors.
 30. The non-transitorycomputer-readable storage medium of claim 29, wherein the at least onemotion sensor includes at least one of an inertial motion unit, anaccelerometer, a magnetometer, and a gyroscope.
 31. The non-transitorycomputer-readable storage medium of claim 29, wherein the one or morebiological sensors includes a photoplethysmography monitor, and thebiological data comprises at least one of blood oxygen saturation data,heart rate data, heart rate variation data, blood pressure data, andrespiration rate data.
 32. The non-transitory computer-readable storagemedium of any one of claim 25, further comprising processing at leastone of the motion data and the biological data using a machine learningclassification technique.
 33. The non-transitory computer-readablestorage medium of any one of claim 25, wherein the transmittednotification to the user comprises generating a graphic display.
 34. Thenon-transitory computer-readable storage medium of claim 33, wherein thegraphic display indicates a number of drinks taken by the user.
 35. Thenon-transitory computer-readable storage medium of claim 33, wherein thegraphic display indicates the user hydration level over time.
 36. Thenon-transitory computer-readable storage medium of claim 25, whereinmotion data, biological data, or both motion data and biological dataare used to identify the drink.